Prosthesis-Aware 3D Human Pose Estimation: A Dataset and Benchmark for RSP Users

Recovering 3D human body motion from video is impor-tant for applications such as rehabilitation assessment and sports per-formance evaluation. For prosthesis users, this requires capturing bothnatural body joints and the geometry of the prosthetic device, a challengethat existing methods are not designed to address. Model-based estima-tors rely on body models trained on non-amputee individuals and cannotrepresent prosthesis geometry, while model-free methods lack body kine-matic priors and are unreliable under occlusion. This challenge is partic-ularly prominent for users of running-specific prostheses (RSPs), wherethe RSP has a complex curved geometry and moves dynamically duringexercise. To fill this gap, we collect RSP3D, the first 3D dataset of RSPusers, covering essential daily-life and exercise actions from participantswith varied amputation conditions, using a multi-camera marker-basedmotion capture setup. We formally define the task of prosthesis-aware 3Dpose estimation, evaluate representative methods in a zero-shot setting,and confirm their individual limitations. We further propose a hybridbaseline combining model-based body joint estimation with model-freeRSP shape recovery, establishing a starting point for future research.Our project page is available at https://ut-vision.github.io/RSP3D/